**Table 1.**

*Adapted from [22].*

They also acknowledged that uncertainties always exist in marketing information as a result of errors, biases, or intentionally designed fault data.

Ref. [27] acknowledging that econometric tests of all the hypotheses on the form of consumer good PLCs have not been carried out at the time adopted a more mathematical method, providing several methods of estimating PLCs. One of the methods provided by [27] is [28]'s generalised least squares method which is plagued with some disadvantages as there is no description of a method for the derivation of significance limits for the parameters. Another problem with Marquardt's method is the difficulty in implementation.

Another method developed by [27] Brockhoff, (1967) is the iterative method which according to the author, provides a good foundation for [28]'s generalised least squares method. This method provides a good starting point, in that it helps eliminate the problem of parameter limits encountered in Marquardt's method. However, it may still be riddled with the other limitations identified with Marquardt's method beyond the parameter limits problem.

Ref. [29] discussed the purpose and usage of the PLC regarding the consumer durable goods industries. The authors focused on a model that could forecast the industry volumes of a newly introduced product through each stage of its life cycle. The model or industry volume is the sum of the original purchases and replacement purchases as given by:

PLC = (Original Purchases) + (Replacements).

It0 = [(U × S)t0 - (U × S)t-1] + K[(Ut0 × S t0).

I = Total industry volume.

U = Universe (Example: households or demographic segments).

K = Replacement constant [K = 1/n(R)].

S = Saturation.

R = Percentage of owners who will replace.

n = Number of periods to replace.

t0 = Current period.

t−1 = First preceding period.

According to [29], product sales volume is composed of two elements: initial purchases or saturation of the product's target universe, and replacements of worn out units, been broken units or obsolete ones. In the early PLC stages, initial purchases constitute the majority of sales volume; however, as ultimate saturation is reached, the replacement component usually becomes dominant.

Ref. [20] reports that that some researchers due to the problem of management having to make different decisions at each LC stage, proposed a different set of forecasting procedures. The evidence used to support these as [20] identified are two products of Corning Glass Works – glass components for colour television tubes and cookware. The reviewing authors cautioned that the recommendations of such works are grounded on inadequate empirical evidence, noting also that the user has

*Understanding the Stages of the Product Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.99036*

to know the PLC stage the product is in before the corresponding set of forecasting procedures can be adopted.

Other researchers according to the same review by [20] developed new product models which forecast the growth and maturity stages of a new product based on either test market data or pre-test research. However, these models are limited in accurately forecasting the second half of the PLC curve [20].

Some other authors/researchers chose to ask the companies producing, managing and marketing the products which life cycle stage their product(s) was in after carefully describing the product life cycle stages and their corresponding characteristics to the respondents. Where the respondents are fully engaged with the product (production managers for example and more closely so in the case of this research); i.e. overseeing a production process, drawing up and implementing a production schedule, managing costs, supervision duties, team building/management and as discovered through this research, duties more closely integrated with functions such as marketing, sales as well as finance this method of LC stage detection could be dependable because the respondents have sufficient relevant knowledge. However, this method could be subjective especially when there's not ample knowledge on the part of the respondents for a number of reasons which may include time spent in a particular company and managing a particular product or group of products.
